The present invention relates to image registration and segmentation and, more specifically, to joint registration and segmentation of images using deep learning.
Being able to track the progression of a disease is an important tool in providing healthcare. Medical images such as MRIs and CT scans taken over a period of time may be used to show the progression of a disease. However, it is difficult to automatically track and interpret the progression owing to problems associated with being able to register and segment the set of medical images that have been acquired over time.
Image registration is the process of interpreting several images to a common coordinate system. For example, where a set of medical images have been acquired over a period of time, it is unlikely that every image will show the same anatomy in the same relative location within the image frame. Registration may therefore be used to place every image into a common frame of reference so that the images may be used to show how a disease has progressed.
Image segmentation is the process of interpreting an image to identify the boundaries of one or more segments. For example, segmentation may be used to identify various landmarks and anatomical structures within the image.
Image segmentation results may be useful in performing image registration, as the various landmarks and anatomical structures, once identified, may be used to help establish how the segmented image should fit into the common set of coordinates. Accordingly, image segmentation is often performed before image registration.